Hierarchical Traffic State Fusion for Infrastructure Assisted Vehicle Speed Regulation of Autonomous Electric Vehicles

Infrastructure-assisted autonomous driving has emerged as a promising approach for enhancing the situational awareness and operational efficiency of Autonomous Electric Vehicles (AEVs) in structured environments. However, existing studies often address traffic perception, wireless communication, and vehicle control as separate components, with limited emphasis on an integrated perception–communication–control framework for traffic-aware speed regulation. This paper presents a Hierarchical Traffic State Fusion Vehicle-to-Infrastructure (HTSF-V2I) framework that integrates multi-camera roadside traffic perception, edge artificial intelligence, long-range LoRa communication, onboard LiDAR sensing, and traffic-state-dependent vehicle speed regulation using a lightweight embedded architecture. Three roadside cameras equipped with an optimized lightweight YOLOv8-based detector monitor campus traffic and estimate local traffic states. A deterministic HTSF module combines the multi-camera traffic states into a unified infrastructure-level traffic advisory, which is transmitted to the AEV through LoRa-based V2I communication. Onboard LiDAR provides local obstacle verification and overrides the infrastructure-derived propulsion command when an obstacle is detected within the defined safety threshold. For smart-campus evaluation, a dataset comprising 5,914 manually annotated traffic images was developed for training and evaluating the traffic perception module. The standard YOLOv8n baseline achieved an mAP@50 of 88.3% at an input resolution of 640× 640 , whereas the deployed 320× 320 CampusLite configuration achieved an mAP@50 of 70.3%, providing a practical trade-off between detection accuracy and computational efficiency for embedded deployment. Experimental evaluation demonstrates multi-camera traffic perception, infrastructure-level traffic-state estimation, end-to-end LoRa-based traffic-advisory transmission, and vehicle propulsion-command selection using infrastructure-derived traffic information with local obstacle supervision. The results demonstrate the feasibility of integrating infrastructure perception, deterministic traffic-state fusion, wireless communication, and onboard safety intelligence within a lightweight framework for low-speed AEV operation in smart-campus environments. The proposed HTSF-V2I framework provides a computationally practical approach for infrastructure-assisted, traffic-aware speed regulation on resource-constrained embedded platforms.